.

nbs/02_utils.ipynb

@@ -39,13 +39,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'torch'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[15], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#| export\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m \n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnn\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mIdentity\u001b[39;00m(nn\u001b[38;5;241m.\u001b[39mModule):\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
+     ]
+    }
+   ],
    "source": [
     "#| export\n",
     "import torch \n",
     "import torch.nn as nn\n",
+    "\n",
+    "class Identity(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Identity, self).__init__()\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        return x\n",
+    "    \n",
+    "\n",
     "class SiameseNetwork(nn.Module):\n",
     "    def __init__(self, model, embedding_size=2000):\n",
     "        super(SiameseNetwork, self).__init__()\n",
@@ -53,8 +74,10 @@
     "        self.backbone = model\n",
     "        self.rgb_grayscale = nn.Conv2d(1,3,kernel_size=3,stride=1,padding=1)\n",
     "        self.a = nn.Sigmoid()\n",
-    "        self.fc1 = nn.Linear(in_features=1000, out_features=embedding_size)        \n",
+    "        self.fc1 = nn.Linear(in_features=1280, out_features=embedding_size)        \n",
     "        self.fc2 = nn.Linear(in_features=embedding_size,out_features=1)    \n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.backbone.classifier = Identity()\n",
     "\n",
     "    def forward_once(self, x):\n",
     "        \n",
@@ -67,10 +90,46 @@
     "        \n",
     "        x1 = self.forward_once(x1)\n",
     "        x2 = self.forward_once(x2)\n",
-    "        out1, out2 = self.fc1(x1), self.fc1(x2)\n",
-    "        dis = torch.abs(torch.sub(out1, out2))\n",
-    "        \n",
-    "        return self.a(self.fc2(dis)).squeeze()"
+    "        out1, out2 = self.relu(self.fc1(x1)), self.relu(self.fc1(x2))\n",
+    "        dis = torch.abs((out1 - out2))\n",
+    "        pred = self.a(self.fc2(dis)).squeeze()\n",
+    "        return pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "255\n",
+      "0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "p = \"/Users/markus/code/explore/signify/results/media/documents/ajy01c00.png\"\n",
+    "pnew = \"/Users/markus/code/explore/signify/results/media/documents/3.png\"\n",
+    "\n",
+    "img = 255 - cv2.imread(p) \n",
+    "print(img.max())\n",
+    "print(img.min())\n",
+    "cv2.imwrite(pnew, img)"
    ]
   },
   {
@@ -86,18 +145,26 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.11.3 ('siglit')",
+   "display_name": "Python 3.10.8 ('base')",
    "language": "python",
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.3"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "58fbb3b322f4fd165dbe2df418fd689197a633c5b2065049c07cf881fd363779"
+    "hash": "a2b499d73ef2075ded5123ca15bce54816687eb05fa98ad819942f2bd35191e9"
    }
   }
  },

signify/_modidx.py

@@ -298,26 +298,11 @@ d = { 'settings': { 'branch': 'main',
                                  'signify.siamese.with_cbs': ('siamese.html#with_cbs', 'signify/siamese.py'),
                                  'signify.siamese.with_cbs.__call__': ('siamese.html#with_cbs.__call__', 'signify/siamese.py'),
                                  'signify.siamese.with_cbs.__init__': ('siamese.html#with_cbs.__init__', 'signify/siamese.py')},
-            'signify.utils': { 'signify.utils.SiameseNetwork': ('utils.html#siamesenetwork', 'signify/utils.py'),
+            'signify.utils': { 'signify.utils.Identity': ('utils.html#identity', 'signify/utils.py'),
+                               'signify.utils.Identity.__init__': ('utils.html#identity.__init__', 'signify/utils.py'),
+                               'signify.utils.Identity.forward': ('utils.html#identity.forward', 'signify/utils.py'),
+                               'signify.utils.SiameseNetwork': ('utils.html#siamesenetwork', 'signify/utils.py'),
                                'signify.utils.SiameseNetwork.__init__': ('utils.html#siamesenetwork.__init__', 'signify/utils.py'),
                                'signify.utils.SiameseNetwork.forward': ('utils.html#siamesenetwork.forward', 'signify/utils.py'),
-                               'signify.utils.SiameseNetwork.forward_once': ('utils.html#siamesenetwork.forward_once', 'signify/utils.py')},
-            'signify.yolo.detect': {},
-            'signify.yolo.models.common': {},
-            'signify.yolo.models.experimental': {},
-            'signify.yolo.models.export': {},
-            'signify.yolo.models.yolo': {},
-            'signify.yolo.utils.activations': {},
-            'signify.yolo.utils.autoanchor': {},
-            'signify.yolo.utils.aws.resume': {},
-            'signify.yolo.utils.datasets': {},
-            'signify.yolo.utils.flask_rest_api.example_request': {},
-            'signify.yolo.utils.flask_rest_api.restapi': {},
-            'signify.yolo.utils.general': {},
-            'signify.yolo.utils.google_utils': {},
-            'signify.yolo.utils.loss': {},
-            'signify.yolo.utils.metrics': {},
-            'signify.yolo.utils.plots': {},
-            'signify.yolo.utils.torch_utils': {},
-            'signify.yolo.utils.wandb_logging.log_dataset': {},
-            'signify.yolo.utils.wandb_logging.wandb_utils': {}}}
+                               'signify.utils.SiameseNetwork.forward_once': ( 'utils.html#siamesenetwork.forward_once',
+                                                                              'signify/utils.py')}}}

signify/utils.py

@@ -1,11 +1,20 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/02_utils.ipynb.
 
 # %% auto 0
-__all__ = ['SiameseNetwork']
+__all__ = ['Identity', 'SiameseNetwork']
 
 # %% ../nbs/02_utils.ipynb 1
 import torch 
 import torch.nn as nn
+
+class Identity(nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+        
+    def forward(self, x):
+        return x
+    
+
 class SiameseNetwork(nn.Module):
     def __init__(self, model, embedding_size=2000):
         super(SiameseNetwork, self).__init__()
@@ -13,8 +22,10 @@ class SiameseNetwork(nn.Module):
         self.backbone = model
         self.rgb_grayscale = nn.Conv2d(1,3,kernel_size=3,stride=1,padding=1)
         self.a = nn.Sigmoid()
-        self.fc1 = nn.Linear(in_features=1000, out_features=embedding_size)        
+        self.fc1 = nn.Linear(in_features=1280, out_features=embedding_size)        
         self.fc2 = nn.Linear(in_features=embedding_size,out_features=1)    
+        self.relu = nn.ReLU()
+        self.backbone.classifier = Identity()
 
     def forward_once(self, x):
         
@@ -27,7 +38,7 @@ class SiameseNetwork(nn.Module):
         
         x1 = self.forward_once(x1)
         x2 = self.forward_once(x2)
-        out1, out2 = self.fc1(x1), self.fc1(x2)
-        dis = torch.abs(torch.sub(out1, out2))
-        
-        return self.a(self.fc2(dis)).squeeze()
+        out1, out2 = self.relu(self.fc1(x1)), self.relu(self.fc1(x2))
+        dis = torch.abs((out1 - out2))
+        pred = self.a(self.fc2(dis)).squeeze()
+        return pred